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Matthias Ziegler

Researcher at HRL Laboratories

Publications -  38
Citations -  13952

Matthias Ziegler is an academic researcher from HRL Laboratories. The author has contributed to research in topics: Workload & Task (project management). The author has an hindex of 12, co-authored 37 publications receiving 12477 citations. Previous affiliations of Matthias Ziegler include University of Texas MD Anderson Cancer Center & Lockheed Martin Advanced Technology Laboratories.

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Patent

System and method for assistive gait intervention and fall prevention

TL;DR: In this paper, a system for system for gait intervention and fall prevention is described, which is incorporated into a body suit having a plurality of distributed sensors and a vestibulo-muscular biostim array.
Patent

System of providing medical treatment

TL;DR: In this article, the authors describe methods for locating a treatment device disposed within a living body by means of magnetic fields that are produced by Barkhausen jumps, principally from amorphous tag wires with high permeability that exhibit reentrant flux reversal.
Book ChapterDOI

Eye Tracking-Based Workload and Performance Assessment for Skill Acquisition

TL;DR: In this preliminary analysis, it is found that measures of saccades, fixations, and pupil diameters significantly correlated with task performance over time and at different difficulties, indicating the validity of the task battery as well as the specificity of workload-related eye tracking measures.
Proceedings ArticleDOI

The neural basis of decision-making during sensemaking: Implications for human-system interaction

TL;DR: The MINDS model (Mirroring Intelligence in a Neural Description of Sensemaking) reveals the neural principles and cognitive tradeoffs that explain weaknesses in human reasoning and decision-making.
Book ChapterDOI

Sensing and Assessing Cognitive Workload Across Multiple Tasks

TL;DR: Workload assessment models are an important tool to develop an understanding of an individual's limitations as discussed by the authors, finding times of excess workload can help prevent an individual from continuing work that may result in human performance issues, such as an increase in errors or reaction time.